To meet the demand for wireless data traffic having increased since deployment of 4th generation (4G) communication systems, efforts have been made to develop an improved 5th generation (5G) or pre-5G communication system. Therefore, the 5G or pre-5G communication system is also called a “beyond 4G network” or a “post long term evolution (LTE) System.”
The 5G communication system is considered to be implemented in higher frequency (mmWave) bands, e.g., 28 GHz or 60 GHz bands, so as to accomplish higher data rates. To decrease propagation loss of the radio waves and increase the transmission distance, the beamforming, massive multiple-input multiple-output (MIMO), full dimensional MIMO (FD-MIMO), array antenna, an analog beam forming, large scale antenna techniques are discussed in 5G communication systems.
In addition, in 5G communication systems, development for system network improvement is under way based on advanced small cells, cloud radio access networks (RANs), ultra-dense networks, device-to-device (D2D) communication, wireless backhaul, moving network, cooperative communication, coordinated multi-points (CoMP), reception-end interference cancellation and the like.
In the 5G system, Hybrid frequency shift keying (FSK) and quadrature amplitude modulation (FQAM) and sliding window superposition coding (SWSC) as an advanced coding modulation (ACM), and filter bank multi carrier (FBMC), non-orthogonal multiple access (NOMA), and sparse code multiple access (SCMA) as an advanced access technology have been developed.
Traditional cellular networks are ‘cell centric’, in the sense that a user equipment (UE) is, in general, tied to one or multiple serving cells, as a consequence of being offered a highest signal to interference and noise ratio (SINR) by a given BS. As the UE approaches the cell edge of this BS, the link quality between the UE and the BS degrades significantly, due to the increased path loss and inter-cell interference (ICI). Such a degradation of link performance is a limiting factor in conventional cell-centric deployment, especially when the network is interference-limited.
5G networks, aiming to provide ubiquitous services to massive number of users, are designed to exploit densification at both network and user equipment levels. In particular, as one of the key technologies in 5G, network virtualization moves the focal point of the network from the cells to the UEs, resulting in the so-called ‘device-centric’ architecture. As opposed to a more traditional cell-centric network, a device-centric network focuses on the UE, which can be surrounded by a number of access points (referred to as base stations BS herein). From the network perspective, because the BSs are so densely deployed, ICI becomes a severe problem. On the other hand, because the UE now has multiple options in terms of which BS to connect to, there exists a problem of which BS the UE may associate with to achieve better performance for the entire network, bearing in mind the interference which could be caused as a result of a UE associating with one or a plurality of BSs.
Node association in a prior art cell-centric network is typically achieved by performing measurements at the UE side, for example, via a received signal strength indicator (RSSI) scan, and the ranks of the cells based on the received signal strength. In other words, in the conventional cell-centric network, the network instructs the UE to connect to the BS (or a BS to send a signal to the UE indicating a connection) that provides the highest SINR for the individual UE. Such a mechanism is more or less effective in the conventional cell centric network, because a UE, when receiving a relatively higher signal from one BS is unlikely to receive high interference from another BS, because the distances between the interfering BS and the UE are sufficiently large due to the large cell size, therefore the interference power is sufficiently small due to path loss.
In a device-centric network, where the UEs and BSs are highly densified, a UE has multiple adjacent BSs, and may also be receiving significant interference from the non-serving BSs. As a result, the prior art mechanism of associating a UE to the BS that is nearest, or provides the highest individual SINR, poses a problem in a dense device-centric network, as it may adversely affect any nearby UEs and so degrade the entire network performance.
Using a highly simplified model, the aforementioned problem is illustrated in FIG. 1. In FIG. 1, consider two UEs, a UE1 and a UE2, that have three surrounding BSs. Note that in practice, there could be a great many more the UEs and the BSs, but for the sake of simplicity, this example may suffice. Suppose the UE2 is already connected to a BS2, as illustrated by the thick line. Now, the UE1 enters the network, and is looking for a BS to connect to. Suppose a BS1 provides the highest SINR to the UE1. In the prior art solution, the network would instruct BS1 to send a signal indicating connection with the UE1. In such a circumstance, however, the UE2 receives a relatively high level of interference from the downlink transmission of the BS1, shown by the dotted line, which has the effect of degrading the overall network throughput, because now the SINR of the UE2 has significantly degraded. In other words, although the best choice in terms of maximizing individual SINR for the UE1 is to connect to the BS1, it does not necessarily mean that this is the optimal choice in terms of achieving the maximum throughput for the entire network, which is indicated, for example, by the average throughput of all the UEs in the network. It is also not fair to UE2 that the service the UE2 receives may be disrupted due to the entry of a new UE-UE1. Note that in FIG. 1, the BSs may use beamforming techniques for downlink transmissions.
Turning to FIG. 2, which illustrates an alternative scenario. If, instead, a UE1 connects to a BS3, it may not achieve the highest individual SINR (compared to connecting to a BS1). However, as a UE 2 now receives significantly less interference, a higher network throughput can be expected from the overall network perspective. It can be seen that the transmission beams from a BS2 directed to the UE2, and from the BS3 directed to the UE 1 do not overlap, resulting in far lower interference than the scenario illustrated in FIG. 1.
FIGS. 1 and 2 also show the network management unit (NMU) 10, which oversees the operation of the entire network. It may be embodied in one or more of many different forms, depending on the specific network requirements.
The example above shows the problem of UE association in a virtualized device-centric network, where, depending on the locations of, and the channels experienced by, the UEs (in relation to each other and the BSs), there exists a ‘best’ or, at least, ‘better’ association mechanism where an optimal throughput of the entire network, rather than just the maximum throughput of an individual UE, can be achieved.
However, obtaining such an optimal solution is non-trivial, especially considering the fact that the network is very dynamic with a large number of UEs entering, leaving, and moving around in the network, in addition to the highly dynamic channels between each BS and UE pair, which makes it extremely difficult to model the environment. Solutions on optimal user association have been looked into, but there are a number of problems when considering application to practical cellular networks. One of the major problems is that the optimization is formulated and solved by considering a static network, i.e., with no changes in locations and activities (e.g. entering or dropping off the network, for instance) of the UEs, or the channels. This is highly impractical as, by the time the optimization algorithm reaches a solution, the network has typically changed so drastically that the solution is no longer optimal.
Although network scheduling can, to some extent, mitigate the interference caused due to simultaneous transmissions, the transmission between BSs to the UEs has to be allocated to different time instants. In 5G, when massive data transmission is required, such a time-division scheduling method will cause delays (therefore higher latency), and require more complicated scheduling mechanisms, which are impractical.
It is also possible, to use coordinated multipoint BS (CoMP) in initial access, where the UE, when entering the network, will determine the ‘best’ BS to attach to, according to, for example, the resulting instantaneous overall network throughput by connecting to all potential BSs. Such an initial access mechanism has three drawbacks: 1) Every time the UE enters the network, the UE has to try out all possible BS connections in order to tell which connection yields the highest network throughput; 2) the decision of connection is made based on instantaneous knowledge of network throughput. A person skilled in the art will appreciate that such a decision is not optimal in terms of achieving the highest average network throughput (averaged over time); 3) the complexity of trying out all possible BSs becomes inhibitive, especially in a device-centric network where the number of BSs is high. For example for a network with N BSs, one UE, when entering the network, has to try out all N possible connections (and requires knowledge of all other UE's quality of connection due to this possible connection) in order to make a decision. As the number of BSs increases, this rapidly becomes impractical.